CTO and Co-Founder, Dynamic Yield
While marketers are working harder to improve audience engagement across channels, CX continues to be a big challenge. To better understand the true ‘State of Customer Experience’ in 2018 and how marketers can leverage technology for a 360-degree view of the customer, we spoke to Dynamic Yield’s CTO, Omri Mendellevich.
Tell us about your role at Dynamic Yield and the team/technology you handle.
Being the world’s first personalization technology stack comes hand in hand with a huge investment in Research & Development. Being CTO at Dynamic Yield has allowed me to lead the amazing people and great minds in our R&D and Product teams. Most of the R&D and Product teams are based in Israel with customer-facing engineers distributed between our New York, Berlin, and Singapore offices. Our technology stack is based on multiple open source projects including Redis, Flink, Kafka, Spark, Hadoop, ElasticSearch and many more. We are constantly working to stay ahead of the curve, trying out new tech, and helping our engineers innovate and disrupt the market.
What is the ‘State of Customer Experience’ in 2018? How do you manage to deliver technology for CX and online personalization?
While companies are working harder than ever to improve interactions at every touchpoint, CX continues to be a game of cat and mouse. For many of the businesses who are just beginning to rise to the occasion, are they’re likely being met with some frustration upon realizing the baseline for customer expectations continues to increase despite their sincere efforts to modernize. And those already delivering an amazing CX know that in order to satiate a customer who grows more demanding every day, the innovation never ends.
At Dynamic Yield, we understand this and our end-to-end personalization platform has been purpose-built to turn every interaction into a meaningful experience — one that evolves with the customer and their changing needs over time.
Tell us about your advanced customer segmentation engine?
Optimal customer segmentation is a critical step towards real personalization. Unfortunately, even though we have more information on an individual than ever, much of it is still trapped within disparate data sources and never fully unified for proper analysis and identification of high-value segments. Even if it is, it often goes untouched (for laborious reasons) and ends up missing the boat when it comes to activating it for the purpose of personalizing experiences.
There are a number of ways Dynamic Yield solves these all too common pain points. First, our platform acts as the source of truth for all customer interactions, onboarding data from across the marketing stack for a 360-degree view of the customer.
Second, our Audience Explorer gives users the ability to analyze segment data in real time without having to fully construct audiences. An instant snapshot of behavior based on simple filters such as device or geography or complex custom actions makes it easy to find valuable segments who contribute to conversion or revenue goals.
Lastly, with our current efforts around Predictive Audiences, the segmentation engine is making a leap into the future, supporting more and more out-of-the-box (OOTB) audiences and uncovering valuable segments hidden within the data. Predictive Audiences allow marketers to discover smarter targeting opportunities and deliver better personalization to new, untapped audience segments, quickly and effortlessly.
How do you leverage AI/ML to create customer segmentation across online journeys?
Machine Learning is assisting our customer segmentation capabilities in two main ways:
- It’s helping our customer understand how an experience should be personalized using our Predictive Targeting engine by recommending courses of action for specific audiences of users.
- It’s creating valuable segments automatically by crunching huge amounts of behavioral data, CRM data, third party data, and just any data point our customer wants to integrate and onboard into the platform.
With the quality of data Dynamic Yield has and our ability to perform the calculations at scale, we are able to provide real, tangible value to our customers, beyond the “buzz” of AI. We don’t believe machines are going to completely replace humans in marketing, but they will provide tremendous support, allowing marketers to make smarter data-driven decisions and deliver the best possible experiences for their customers – at scale.
Tell us more about your partnership with ResponseTap? How would it benefit customers of both companies?
Lots of platforms say they close the attribution loop with offline channels but actually can’t. Our partnership with ResponseTap does just that, allowing customers, like Click&Go, one of Europe’s largest travel providers, to sync call center sessions to the rest of a user’s online experience. ResponseTap sends information on call center interactions to our platform and we then use it to personalize. Once we do, call center agents receive data in real time, empowering them with the information they need to go 1:1 with every customer.
For some industries, call centers remain a major point of customer experience and interaction and we’re excited to be working with ResponseTap to deliver on true personalization across channels.
How have tools and technologies to measure customer journey analytics evolved with new-age attribution models?
As the need for analyzing touchpoints along the customer journey became a necessity, naturally the tools and models for determining the relative contribution of each activity would have to evolve somewhat simultaneously. Though, many solutions which should support this theory still rely on and push single-session attribution for short-term experience optimization. And as a result, marketers embrace misleading tactics that undermine the effectiveness of their marketing campaigns.
How does Dynamic Yield impact sales velocity and sales discovery campaigns across various stages of customer journeys?
Agility and speed to market is such a critical element of capitalizing on the personalization opportunity. It’s about taking the data you have and acting on it at the right time for the most impactful results. But this isn’t always easy for some, as launching new campaigns can fall prey to design and development cycles that create bottlenecks in the workflow. And that translates into missing deadlines and low-impact campaigns.
With the Personalization Template Library, we created, teams can gain inspiration from a collection of dozens of pre-built experiences, select the one that matches their needs, easily customize them, and set them live. Instead of starting from scratch, they can recoup valuable hours potentially lost going back and forth with various stakeholders and put them back into driving more revenue through consistent and ongoing campaign creation.
Why do marketers still find it hard to attribute customer success to experience management? What are your recommendations to overcome these challenges?
The buyer’s journey in today’s fragmented marketing environment is nonlinear, and it often consists of multiple touchpoints. As a result, being able to attribute conversions and revenue uplifts to specific experiences has become a huge challenge for marketers.
But we know proper attribution allows us to get a better understanding of the influence of our actions on buyer decisions to ultimately serve the highest converting experience. And this largely depends on the content and context of that specific experience. Therefore, moving beyond strict conversion attribution models to flexible attributions is vital to help marketers know what is truly influencing conversion to optimize accordingly.
To make sure that our customers are correctly associating conversions to a given experiment or personalized experience, Dynamic Yield offers flexible conversion attribution configurations, either on scope (session, version of experience, lifetime) or the attribution trigger (impressions, click, or any custom event).
Thanks for chatting with us, Omri.
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